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Applied Sciences
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Advanced Technologies and Applications of High-Performance Computing and Parallel Computing
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Advanced Technologies and Applications of High-Performance Computing and Parallel Computing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 1772

Special Issue Editors

Prof. Dr. Yunquan Zhang

E-Mail Website
Guest Editor
Institute of Computing Technology, Chinese Academy of Science, Beijing 100190, China
Interests: parallel computing; parallel programming; parallel computational model
Special Issues, Collections and Topics in MDPI journals
Dr. Liang Yuan

E-Mail Website
Guest Editor
Institute of Computing Technology, Chinese Academy of Science, Beijing 100190, China
Interests: parallel computational model
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Applied Sciences is a semi-monthly peer-reviewed, open access journal which provides an advanced forum for studies related to all aspects of applied physics, applied chemistry, applied biology, and engineering, environmental, and Earth sciences. It is free for readers and indexed within SCIE, Scopus, ESCI (Web of Science), Ei Compendex, MathSciNet, and many other databases. For more information, please check: https://www.mdpi.com/journal/applsci.

This Special Issue, “Advanced Technologies and Applications of High Performance Computing and Parallel Computing” of Applied Sciences, invites original, high-quality work presenting novel research on high-performance computing. Featured articles should present innovative strategies that address issues in different aspects of performance, such as parallelization, evaluation, algorithm, programming models, autotuning, co-design, and benchmarks.

Prof. Dr. Yunquan Zhang
Dr. Liang Yuan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • performance optimization
  • performance evaluation
  • parallel algorithms
  • parallel programming models
  • HPC applications
  • HPC in AI
  • big data
  • hardware/software co-design
  • performance and energy efficiency/benchmarks
  • performance tuning

Published Papers (2 papers)

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Research

16 pages, 563 KiB  
Article
An Asynchronous Parallel I/O Framework for Mass Conservation Ocean Model
by Renbo Pang, Fujiang Yu, Yu Zhang and Ye Yuan
Appl. Sci. 2023, 13(24), 13230; https://doi.org/10.3390/app132413230 - 13 Dec 2023
Viewed by 590
Abstract
I/O is often a performance bottleneck in global ocean circulation models with fine spatial resolution. In this paper, we present an asynchronous parallel I/O framework and demonstrate its efficacy in the Mass Conservation Ocean Model (MaCOM) as a case study. By largely reducing [...] Read more.
I/O is often a performance bottleneck in global ocean circulation models with fine spatial resolution. In this paper, we present an asynchronous parallel I/O framework and demonstrate its efficacy in the Mass Conservation Ocean Model (MaCOM) as a case study. By largely reducing I/O operations in computing processes and overlapping output in I/O processes with computation in computing processes, this framework significantly improves the performance of the MaCOM. Through both reordering output data for maintaining data continuity and combining file access for reducing file operations, the I/O optimizing algorithms are provided to improve output bandwidth. In the case study of the MaCOM, the cost of output in I/O processes can be overlapped by up to 99% with computation in computing processes as decreasing output frequency. The 1D data output bandwidth with these I/O optimizing algorithms is 3.1 times faster than before optimization at 16 I/O worker processes. Compared to the synchronous parallel I/O framework, the overall performance of MaCOM is improved by 38.8% at 1024 computing processes for a 7-day global ocean forecast with 1 output every 2 h through the asynchronous parallel I/O framework presented in this paper. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications of High-Performance Computing and Parallel Computing)
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19 pages, 1494 KiB  
Article
Exploiting Data Similarity to Improve SSD Read Performance
by Shiqiang Nie, Jie Niu, Zeyu Zhang, Yingmeng Hu, Chenguang Shi and Weiguo Wu
Appl. Sci. 2023, 13(24), 13017; https://doi.org/10.3390/app132413017 - 06 Dec 2023
Viewed by 912
Abstract
Although NAND (Not And) flash-based Solid-State Drive (SSD) has recently demonstrated a significant performance advantage against hard disk, it still suffers from non-negligible performance under-utilization issues as the access conflict often occurs during servicing IO requests due to the share mechanism (e.g., several [...] Read more.
Although NAND (Not And) flash-based Solid-State Drive (SSD) has recently demonstrated a significant performance advantage against hard disk, it still suffers from non-negligible performance under-utilization issues as the access conflict often occurs during servicing IO requests due to the share mechanism (e.g., several chips share one channel bus, several planes share one data register inside the die). Many research works have been devoted to minimizing access conflict by redesigning IO scheduling, cache replacement, and so on. These works have achieved reasonable results; however, the potential data similarity characterization is not utilized fully in prior works to alleviate access conflict. The basic idea is that, as data duplication is common in many workloads where data with the same content from different requests could be distributed to the address with minimized access conflict (i.e., the address does not share the same channel or chip), the logic address is mapped to more than one physical address. Therefore, the data can be read out from candidate pages when the channel or chip of its original address is busy. Motivated by this idea, we propose Data Similarity aware Flash Translation Layer (DS-FTL), which mainly includes a content-aware page allocation scheme and a multi-path read scheme. The DS-FTL enables maximization of the channel-level and chip-level parallelism and avoids the read stall induced by bus-shared mechanisms. We also conducted a series of experiments on SSDsim, with the subsequent results depicting the effectiveness of our scheme. Compared with the state-of-art, our scheme reduces read latency by 35.3% on average in our workloads. Full article
(This article belongs to the Special Issue Advanced Technologies and Applications of High-Performance Computing and Parallel Computing)
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